Multimodal Intelligent Healthcare and Big Data Analysis
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Bioelectronics".
Deadline for manuscript submissions: 15 July 2026 | Viewed by 23
Special Issue Editors
Interests: generative AI; healthcare AI; medical imaging
Interests: healthcare AI; medical imaging; deep learning
Special Issues, Collections and Topics in MDPI journals
Interests: machine learning; deep learning; computer vision; human motion analysis
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
With the rapid development of artificial intelligence and biomedical data acquisition technologies, the field of intelligent healthcare is entering a new era of data-driven diagnosis and treatment. However, single-modality information such as medical imaging, clinical records, or genomic data often provides only a partial view of patient health. Multimodal analysis—integrating heterogeneous data sources—has become essential for comprehensive disease understanding, precision medicine, and early diagnosis. At the same time, the increasing scale and complexity of medical data pose challenges for effective data fusion, knowledge representation, and trustworthy decision making.
This Special Issue aims to explore cutting-edge advances in multimodal intelligent healthcare and big data analysis, focusing on novel methodologies, algorithms, and applications that bridge medical imaging, clinical informatics, and computational modeling. The scope aligns closely with the journal’s mission to disseminate interdisciplinary research that advances data-driven healthcare, intelligent systems, and biomedical engineering. Contributions that demonstrate translational potential or real-world clinical impact are particularly encouraged.
In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:
- Multimodal data fusion for medical diagnosis and prognosis;
- Deep learning and generative models for medical image analysis;
- Cross-domain and cross-institutional generalization in healthcare AI;
- Federated and privacy-preserving medical data analytics;
- Big data frameworks for large-scale biomedical information processing;
- Trustworthy, explainable, and ethical AI in clinical applications;
- Multimodal biomarkers for personalized and precision medicine.
We look forward to receiving your contributions.
Dr. Guipeng Lan
Dr. Shuai Xiao
Prof. Dr. Qinggang Meng
Guest Editors
Manuscript Submission Information
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Keywords
- multimodal learning
- medical image analysis
- healthcare data fusion
- clinical decision support systems
- big data in healthcare
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